Cervical Cell Segmentation Based on Improvd Mask R-CNN Model
In the process of cervical cell segmentation ,the original Mask R-CNN model utilizes ResNet50 and FPN as the feature extraction networks yielding satisfactory segmentation results. However ,there still exist problems such as slow segmentation speed and suboptimal edge segmentation. To address these issues ,an improved Mask R-CNN model is proposed. Firstly ,the model employs the lightweight MobileNet V2 as the feature extraction module ,which significantly reduces the amount of model parameters and provides the possibility of real-time segmentation for cervical cell images. Secondly ,an attention module is integrated into the feature extraction network ,which maximizes the access to the underly-ing information through the adaptive feature optimization function. Lastly ,the model adopts the skip connection in the mask generation stage to effectively fuse the information of various scales ,which improves the ability of acquiring information of the network. Experimental results demonstrate that the proposed model has increased the segmentation speed of cervical cell nuclei by about 50％ and the segmentation accuracy by 7％.
万方数据
维普
